Impaired Rest-Activity Rhythm Characteristics Predict Higher Risk of Incident Type 2 Diabetes in UK Biobank Participants

Authors

• Chris Ho Ching Yeung
• Alison K Wright
• Daniel P Windred
• Angus C Burns
• Andrew J K Phillips
• Sean W Cain
• Martin K Rutter
• Qian Xiao

Language

English

Publication Date

8-1-2025

Journal

Diabetes Care

DOI

10.2337/dc25-0309

PMID

40569828

PMCID

PMC12281981

PubMedCentral® Posted Date

6-26-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Objective: Circadian rhythms play a key role in metabolic health. Rest-activity rhythms, which are in part driven by circadian rhythms, may be associated with diabetes risk. There is a need for large prospective studies to comprehensively examine different rest-activity metrics to determine their relative strength in predicting risk of incident type 2 diabetes.

Research design and methods: In actigraphy data from 83,887 UK Biobank participants, we applied both parametric and nonparametric algorithms to derive 13 different metrics characterizing different aspects of rest-activity rhythm. Diabetes cases were identified using both self-reported data and health records. We used Cox proportional hazards models to assess associations between rest-activity parameters and type 2 diabetes risk and random forest models to determine the relative importance of these parameters in risk prediction.

Results: We found that multiple rest-activity characteristics were predictive of a higher risk of incident diabetes, including lower pseudo-F statistic (hazard ratio [HR] of quintile 1 [Q1] vs. Q5 1.27; 95% CI 1.09-1.46; Ptrend < 0.001), lower amplitude (HRQ1 vs. Q5 2.56; 95% CI 2.21-2.97; Ptrend < 0.001), lower midline estimating statistic of rhythm (HRQ1 vs. Q5 2.59; 95% CI 2.24-3.00; Ptrend < 0.001), lower relative amplitude (HRQ1 vs. Q5 4.64; 95% CI 3.74-5.76; Ptrend < 0.001), lower M10 (HRQ1 vs. Q5 3.82; 95% CI 3.20-4.55; Ptrend < 0.001), higher L5 (HRQ5 vs. Q1 1.88; 95% CI 1.62-2.19; Ptrend < 0.001), and later L5 start time (HRQ5 vs. Q1 1.20; 95% CI 1.04-1.38; Ptrend = 0.004). Random forest models ranked most of the rest-activity metrics as top predictors of diabetes incidence, when compared with traditional diabetes risk factors. The findings were consistent across subgroups of age, sex, BMI, and shift work status.

Conclusions: Rest-activity rhythm characteristics measured from actigraphy data may serve as digital biomarkers for predicting type 2 diabetes risk.

Keywords

Humans, Diabetes Mellitus, Type 2, Male, Female, Middle Aged, United Kingdom, Aged, Rest, Circadian Rhythm, Risk Factors, Biological Specimen Banks, Prospective Studies, Actigraphy, UK Biobank

Published Open-Access

yes

Recommended Citation

Yeung, Chris Ho Ching; Wright, Alison K; Windred, Daniel P; et al., "Impaired Rest-Activity Rhythm Characteristics Predict Higher Risk of Incident Type 2 Diabetes in UK Biobank Participants" (2025). Faculty, Staff and Student Publications. 1579.
https://digitalcommons.library.tmc.edu/uthsph_docs/1579